Exemplar Model Research Articles

Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

AbstractLarge‐scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust‐climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo‐based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

Testing formal cognitive models of classification and old-new recognition in a real-world high-dimensional category domain

Categorization and old-new recognition memory are closely linked topics in the cognitive-psychology literature and there have been extensive past efforts at developing unified formal modeling accounts of these fundamental psychological processes. However, the existing formal-modeling literature has almost exclusively used small sets of simplified stimuli and artificial category structures. The present work extends this literature by collecting both categorization and old-new recognition judgments on a large set of high-dimensional stimuli that form real-world category structures: namely, a set of 540 images of rocks belonging to the geologically-defined categories igneous, metamorphic and sedimentary. Participants first engaged in a learning phase in which they classified large sets of training instances into these real-world categories. This was followed by a test phase in which they classified both training and novel transfer items into the learned categories and also judged whether each item was old or new. We attempted to model both the classification and recognition test data at the level of individual items. Ultimately, the categorization data were well fit by both an exemplar and clustering model, but not by a prototype model. Only the exemplar model was able to provide a reasonable first-order account of the old-new recognition data; however, the standard version of the model failed to capture the variability in hit rates within the class of old-training items themselves. An extended hybrid-similarity version of the exemplar model that made allowance for boosts in self-similarity due to matching distinctive features yielded much improved accounts of the old-new recognition data. The study is among the first to test cognitive-process models on their ability to account quantitatively for old-new recognition of real-world, high-dimensional stimuli at the level of individual items.

State-of-the-art 2023 on gene therapy for phenylketonuria.

Phenylketonuria (PKU) or hyperphenylalaninemia is considered a paradigm for an inherited (metabolic) liver defect and is, based on murine models that replicate all human pathology, an exemplar model for experimental studies on liver gene therapy. Variants in the PAH gene that lead to hyperphenylalaninemia are never fatal (although devastating if untreated), newborn screening has been available for two generations, and dietary treatment has been considered for a long time as therapeutic and satisfactory. However, significant shortcomings of contemporary dietary treatment of PKU remain. A long list of various gene therapeutic experimental approaches using the classical model for human PKU, the homozygous enu2/2 mouse, witnesses the value of this model to develop treatment for a genetic liver defect. The list of experiments for proof of principle includes recombinant viral (AdV, AAV, and LV) and non-viral (naked DNA or LNP-mRNA) vector delivery methods, combined with gene addition, genome, gene or base editing, and gene insertion or replacement. In addition, a list of current and planned clinical trials for PKU gene therapy is included. This review summarizes, compares, and evaluates the various approaches for the sake of scientific understanding and efficacy testing that may eventually pave the way for safe and efficient human application.

Phonetic Dissimilarity and L2 Category Formation in L2 Accommodation.

Many studies of speech accommodation focus on native speakers with different dialects, whereas only a limited number of studies work on L2 speakers' accommodation and discuss theories for second language (L2) accommodation. This paper aimed to fill the theoretical gap by integrating the revised speech learning model (SLM) with the exemplar-based models for L2 speech accommodation. A total of 19 Cantonese-English bilingual speakers completed map tasks with English speakers of Received Pronunciation and General American English in two separate experiments. Their pronunciations of THOUGHT and PATH vowels, and fricatives [z] and [θ] were examined before, during, and after the map tasks. The role of phonetic dissimilarity in L2 accommodation and L2 category formation in the revised SLM (SLM-r) were tested. First, the results suggested that global phonetic dissimilarity cannot predict Hong Kong English (HKE) speakers' accommodation patterns. Instead, the segment-specific phonetic dissimilarity between participants and interlocutors was found to be positively correlated with the participants' degree of accommodation. In addition, HKE speakers who did not form a new L2 category of [z] were found to significantly accommodate toward their interlocutor, suggesting that L2 accommodation might not be constrained by phonological category. An integrated exemplar model for L2 accommodation is proposed to explain these findings.

Advancement of phonetics in the 21st century: Exemplar models of speech production

In the first decades of the 21st century, exemplar theory has fueled an explosion of theoretical and empirical work in speech production. We review the foundations for this framework in linguistics and cognitive science, and examine how recent empirical findings challenge core principles of exemplar theory. While theoretical advances in hybrid exemplar models address some of these issues, accounting for the emergence of structure, the incorporation of structure into exemplar updating, and the non-uniformity of phonetic variation and convergence (among other phenomena), remain major challenges for current models. We discuss future directions for developing exemplar theories as comprehensive accounts of speech production.

Further tests of sequence-sensitive models in a modified garner task using separable dimensions.

In the study of perceptual categorization, a key distinction is made between separable and integral dimensions. Separable dimensions are easy to attend in isolation, while integral dimensions are not. Little et al. (2016) showed that when trial-by-trial responses are analyzed, a consistent pattern of sequential effects was found in a modified Garner paradigm using integral-dimension stimuli. The present experiments investigated whether these pronounced sequential effects are also found with separable-dimension stimuli. Four experiments using two different types of separable dimensions were conducted. The results indicated that similar patterns of sequential effects were present for separable-dimension stimuli, but, unlike for integral dimensions, the effect of a change in the irrelevant dimension in the filtering task was not found. Further, for separable dimensions, the overall pattern of sequential effects did not vary between the Garner tasks (i.e., control, correlated, and filtering). To explain these results, we fit a sequence-sensitive exemplar model and compared the fits of this model to a novel sequence-sensitive feature model, in which only the relevant feature influences the categorization decision. We found that the full exemplar model provided a more compelling account of both our separable dimension data and the integral dimension data of Little et al. (2016). These findings provide a more complete understanding of perceptual categorization and add to the growing body of literature on the prevalence and critical implications of strong sequential effects in cognitive tasks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Length of the state trace: A method for partitioning model complexity

A novel and easy-to-compute measure is proposed that compares the relative contribution of each parameter of a mathematical model to the model’s mathematical flexibility or complexity, with respect to accounting for the results of some specific experiment. When the data space is a two-dimensional plot of the type used in standard state-trace analysis, then the model complexity contributed by a single parameter equals the length of the state trace (LOST) that results when that parameter is varied and all other parameters are held constant. For the normal, equal-variance, signal-detection model, the average LOST when the response-criterion parameter XC is varied is about four times greater than the average LOST when the sensitivity parameter d′ is varied. As a result, applying the signal-detection model to random data almost always leads to the conclusion that all the points share the same value of d′ but were generated under different values of XC. Parameters that have non-monotonic effects on performance, such as the attention-weight parameter that is used in popular exemplar and prototype models of categorization, tend to have large LOSTs, and therefore contribute to model flexibility more than parameters that have monotonic effects on performance. Comparing LOSTs for exemplar and prototype models also leads to some deep new insights into the structure of both models.

Hearing emotion in two languages: A pupillometry study of Cantonese–Mandarin bilinguals’ perception of affective cognates in L1 and L2

AbstractDifferential affective processing has been widely documented for bilinguals: L1 affective words elicit higher levels of arousal and stronger emotionality ratings than L2 affective words (Pavlenko, 2012). In this study, we focus on two closely related Chinese languages, Mandarin and Cantonese, whose affective lexicons are highly overlapping, with shared lexical items that only differ in pronunciation across languages. We recorded L1 Cantonese – L2 Mandarin bilinguals’ pupil responses to auditory tokens of Cantonese and Mandarin affective words. Our results showed that Cantonese–Mandarin bilinguals had stronger pupil responses when the affective words were pronounced in Cantonese (L1) than when the same words were pronounced in Mandarin (L2). The effect was most evident in taboo words and among bilinguals with lower L2 proficiency. We discuss the theoretical implications of the findings in the frameworks of exemplar theory and models of the bilingual lexicon.

Integrating Categorization and Decision-Making.

Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were shown six faces varying in width, first asked to categorize the faces, and then decide a course of action for each face. Each experiment was designed to include three groups, and for each group, we manipulated the probabilistic contingencies between stimulus, category assignments, and decision consequences. For each group, each participant received three different sequences of category response, category feedback, decision response, and decision feedback. We found that participants were only partially responsive in the appropriate directions to the contingencies assigned to each group. Comparisons of results from different sequences provided evidence for empirical interference effects of categorization on decisions. The empirical interference effect is defined as the difference between the probability of taking a hostile action in decision-alone conditions and the total probability of taking a hostile action in categorization-decision conditions. To test competing accounts for multiple empirical results, including two-stage choice probabilities and empirical interference effects, we compared a quantum cognition model versus a two-stage exemplar categorization model at both aggregate and individual levels. Using a Bayesian information criterion, we found that the quantum model provided an overall better model fit than the exemplar model. Although both models predicted empirical interference effects, the exemplar model was able to generate probabilistic deviation by incorporating category information of the first stage into the feature representation of the subsequent decision stage, while the quantum model produced interference effect by superposition, measurement, and quantumentanglement.

Bathymetry Retrieval Algorithm Based on Hyperspectral Features of Pure Water Absorption From 570 to 600 nm

Current efforts for improving the hyperspectral optimization processing exemplar (HOPE) model include further testing of remote-sensing reflectance (Rrs) features containing useful information for bathymetry retrieval via the minimization of the interference stemming from the variability in inherent optical properties and benthic reflectance. In this paper, we found a novel feature originating from the pure water absorption within the narrow spectral region of 570–600 nm. In most coastal regions of clear water, for example, in a coral reefs environment, pure water accounts for the majority of the total absorption in this spectral range. In addition to the depth variation, the spectral behavior of Rrs(570–600) is primarily dominated by a steep increase in pure water absorption with wavelength, whereas the influence of other optical properties such as phytoplankton/CDOM absorption, particle backscattering, and benthic reflectance can be simplified using the spectrally constant shape model. A HOPE pure water (HOPE-PW) algorithm using this feature was developed based on Rrs measurements with a spectral resolution of near 3.5 nm, wherein only four unknown parameters must be resolved. The validation from LiDAR data and comparison with HOPE-BRUCE using PRISM data at 15 sites located in five distinct regions of Palau, Guam, Great Barrier Reef, Hawaiian Islands and Florida Key, confirmed that the HOPW-PW yielded a considerable performance and provided adequate transferability to other sites with varying bottom and water environments. The sensitivity analysis based on Hydrolight-simulated datasets showed that HOPE-PW was less effected by bottom type variations but still had limitations in retrieving water optical properties.

Evaluating the current profile of liver resections in the United States: Evolution or standstill

Background: There is increasing attention on strategies to improve patient outcomes and the delivery of surgical services in the United States (US). Liver resections (LR) remain an exemplar model of complex surgery and encompass a broad array of operations from simple excisions to technically challenging reconstructive procedures in specialized units. The purpose of this study was to assess the national profile of LR in the United States and evaluate progress in current practice.

Political Improved Invasive Weed Optimization-Driven Hybrid Exemplar Technique for Video Inpainting Process

Video inpainting aspires to fill the Spatio-temporal holes in videos with probable and coherent content. This process recovers the missing content of corrupted video effectively, which is useful in many fields, including removal of watermarking and video restoration. The difficulties of creating video contents with exquisite detail while maintaining spatiotemporal coherence in the missing areas is the main difficulty in the video inpainting process. Modern studies ignore semantic structural coherence maintenance between frames in favor of using flow information to synthesize temporally smooth pixels. In this paper, Political Improved Invasive Weed Optimization (PIIWO)-based optimal exemplar is designed for the productive video inpainting process. Accordingly, the developed PIIWO algorithm is newly designed by combining Political Optimizer (PO) and Improved Invasive Weed Optimization (IIWO). Here, the inpainting results obtained from context-aware Ant Lion Gray Wolf Optimization (ALGWO)-based Markov Random Field (MRF) modeling, Whale Monarch Butterfly Optimization (Whale MBO)-based Deep Convolutional Neural Network (DCNN), K-Nearest Neighbors (KNN) with Bhattacharya distance, Bi-harmonic function modules and developed PIIWO-based exemplar model are fused using Bayes probabilistic fusion for producing the final result. Three metrics, peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME) and structural similarity (SSIM) of 40.19[Formula: see text]dB, 78.07[Formula: see text]dB and 0.9857, respectively, are used to assess the performance of the developed video inpainting technique.

Contrasting exemplar and prototype models in a natural-science category domain.

A classic issue in the cognitive psychology of human category learning has involved the contrast between exemplar and prototype models. However, experimental tests to distinguish the models have relied almost solely on use of artificially-constructed categories composed of simplified stimuli. Here we contrast the predictions from the models in a real-world natural-science category domain-geologic rock types. Previous work in this domain used a set of complementary methods, including multidimensional scaling and direct dimension ratings, to derive a high-dimensional feature space in which the rock stimuli are embedded. The present work compares the category-learning predictions of exemplar and prototype models that make reference to this derived feature space. The experiments include conditions that should be favorable to prototype abstraction, including use of multiple large-size categories, delayed transfer testing, and real-world category structures. Nevertheless, the results of the qualitative and quantitative model comparisons point toward the exemplar model as providing a far better account of the observed results. Evidence is also provided that participants do not rely on all-or-none rote memories for the stored exemplars but rather use remembered exemplars as a basis for generalizing to novel transfer items from the learned categories. Limitations and directions of future work are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Exemplar-model account of categorization and recognition when training instances never repeat.

In a novel version of the classic dot-pattern prototype-distortion paradigm of category learning, Homa et al. (2019) tested a condition in which individual training instances never repeated, and observed results that they claimed severely challenged exemplar models of classification and recognition. Among the results was a dissociation in which participants classified transfer items with high accuracy in the no-repeat condition, yet in old-new recognition tests showed no ability to discriminate between old and new items of the same level of distortion from the prototype. In addition, speed of classification learning was no faster in a condition in which a small set of training instances was repeated continuously compared with the no-repeat condition. Here we show through computer-simulation modeling that exemplar models naturally capture the classification-recognition dissociation in the no-repeat condition, as well as a wide variety of other qualitative effects reported by Homa et al. (2019). We also conduct new conceptual-replication experiments to investigate their reported null effect of repeated versus nonrepeated training instances on speed of classification learning. In contrast to Homa et al. (2019) we find that speed of learning is substantially faster in the repeat condition than in the no-repeat condition, precisely as exemplar models predict. The exemplar model also captures a wide variety of transfer effects observed following the completion of category learning, including the classification-recognition dissociation observed across the repeat and no-repeat conditions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images.

Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs. normal and BT vs. normal on MRI images. We have developed an exemplar deep feature extraction model to diagnose RCT and BT disorders. The model was tested on a new MR image dataset comprising transverse, sagittal, and coronal MRI images of the shoulder that had been organized into three cases. BT was studied on transverse MRI images (Case 1), while RCT was studied on sagittal (Case 2) and coronal MRI images (Case 3). Our model comprised deep feature generation using a pre-trained VGG19, feature selection using iterative neighborhood component analysis (INCA), and classification using shallow standard classifiers k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). In the feature extraction phase, two fully connected layers were used to extract deep features from the original image, and sixteen fixed-size patches obtained by the division of the original image. This model was named Vision VGG19 (ViVGG), analogous to vision transformers (ViT). The feature vector is extracted from the raw image dataset, and 16 feature vectors are extracted from each fixed-size patch. Seventeen feature vectors obtained from each image are obtained from fc6 and fc7 layers of the pre-trained VGG19, are merged to obtain final feature vector. INCA was used to choose the top features from the created features, and the chosen features were classified using shallow classifiers. We defined three cases to evaluate the proposed ViVGG19 to diagnose RT and BCT disorders. Our proposed ViVGG19 model achieved more than 99% accuracy using the KNN classifier. ViVGG19 is a very effective model for detecting RCT and BT disorders on shoulder MRI images. The developed automated system is ready to be tested with a bigger diverse database obtained from different medical centers.

Bio-optical properties of the Brazilian Abrolhos Bank’s shallow coral-reef waters

The Abrolhos Bank harbors the richest coral reef ecosystem in the South Atlantic Ocean. It exhibits unique geomorphologic structures, is localized in shallow depths, and is divided into two reef regions with an inner arc close to the coast (3–20 m depth) and an outer deeper arc (5–30 m depth). This study aims to describe some bio-optical properties of the Abrolhos Bank waters and to evaluate the performance of the inversion Hyperspectral Optimization Processing Exemplar (HOPE) model, developed to retrieve optical properties in shallow waters, in the region. To this end, measurements at 75 stations during two field campaigns conducted during the 2013 and 2016 wet seasons were analyzed, and the HOPE model was applied to both in situ remote sensing reflectance (Rrs) spectra and PRecursore IperSpettrale della Missione Applicativa (PRISMA) imagery. Significant differences in optical and biological properties were found between the two arcs. The empirical relationships between chlorophyll-a concentration (Chl-a) and absorption coefficient of phytoplankton at 440 nm (aphy(440)) diverged from Bricaud’s models, suggesting differences in phytoplankton diversity and cell size. In both arcs, total non-water absorption coefficient at 440 nm (aT-w(440)) was dominated by colored dissolved organic matter (CDOM) by ∼60%. Absorption coefficient by CDOM (acdom) presented a higher variability within the outer arc, with the lowest contribution from non-algal particles (NAPs), and the spectral slopes of aCDOM resembled those of the inner arc. The spectral slopes of the NAP absorption coefficient suggested a dominance by organic rather than mineral particles that probably originated from biological production. The HOPE model applied to in situ Rrs performed satisfactorily for depth in the Abrolhos Bank waters, although retrievals of aphy(440), CDOM plus NAP (adg(440)) and aT-w(440) were underestimated with a relative bias of −27.9%, −32.1% and −45.8%, respectively. The HOPE model retrievals from the PRISMA image exhibited low aphy(440) values over the whole scene and the highest adg(440) values in the Caravelas river plume. Very shallow depths (≤3 m), bottom substrate reflectance used as input in the HOPE model, model parametrization associated with the water complexity in the study site, and uncertainties associated to Rrs measurements used as input might be responsible for differences found when comparing HOPE retrievals with in situ measurements.

Improving the efficiency of scale-up and deployment of community health workers in Mali: A geospatial analysis.

Optimising the scale and deployment of community health workers (CHWs) is important for maximizing geographical accessibility of integrated primary health care (PHC) services. Yet little is known about approaches for doing so. We used geospatial analysis to model optimised scale-up and deployment of CHWs in Mali, to inform strategic and operational planning by the Ministry of Health and Social Development. Accessibility catchments were modelled based on travel time, accounting for barriers to movement. We compared geographic coverage of the estimated population, under-five deaths, and plasmodium falciparum (Pf) malaria cases across different hypothetical optimised CHW networks and identified surpluses and deficits of CHWs compared to the existing CHW network. A network of 15 843 CHW, if optimally deployed, would ensure that 77.3% of the population beyond 5 km of the CSCom (community health centre) and CSRef (referral health facility) network would be within a 30-minute walk of a CHW. The same network would cover an estimated 59.5% of U5 deaths and 58.5% of Pf malaria cases. As an intermediary step, an optimised network of 4 500 CHW, primarily filling deficits of CHW in the regions of Kayes, Koulikoro, Sikasso, and Ségou would ensure geographic coverage for 31.3% of the estimated population. There were no important differences in geographic coverage percentage when prioritizing CHW scale-up and deployment based on the estimated population, U5 deaths, or Pf malaria cases. Our geospatial analysis provides useful information to policymakers and planners in Mali for optimising the scale-up and deployment of CHW and, in turn, for maximizing the value-for-money of resources of investment in CHWs in the context of the country's health sector reform. Countries with similar interests in optimising the scale and deployment of their CHW workforce may look to Mali as an exemplar model from which to learn.

Identifying conceptual incongruences within global leadership competency models

Despite decades of research on global leadership competencies, there is a continued unresolved debate among scholars around an integrative Global Leadership Competency model (GLC model), as none of the models proposed has been widely used in literature. Attempts to replicate and operationalize existing models are scarce, and scholars tend to recreate GLC models instead of building on existing ones. Instead of proposing yet another GLC model, this paper aims to identify factors that explain the unresolved debate around an integrative Global Leadership Competency model to be used in academic and managerial settings. To do so, we conducted a conceptual analysis based on an integrative literature review, where a sample of exemplar GLC models was surveyed and assessed. This analysis resulted in the identification of three conceptual incongruences, namely 1) varying assumptions regarding the meaning of competency; 2) divergent model structuring; and 3) varying delimitation, resulting in varied sets of competencies. These incongruences relate to diverse assumptions, perspectives and interpretations that are inherent to the development of GLC models, but often not explicitly acknowledged and addressed by studies. We argue that these incongruences hinder scholars’ and practitioners’ capacity to evaluate, compare and contrast different models, and may therefore explain the unresolved debate around an integrative GLC model. While grounded in global leadership theory, this article also contributes to cross-cultural leadership and management scholarship by providing a critical discussion about the competencies required by leaders to operate effectively in a global environment, where they are required to manage across different cultures and socio-economic contexts.

Staging the bipolar disorders: Are early stages at too early a stage for intervention?

A number of staging models have been generated for the bipolar disorders, which include pre-onset as well as post-onset stages. Some models propose treatments for those at the pre-onset stage, a recommendation which is critiqued here. Several exemplar staging models are overviewed, and a critique is provided. The critique argues against intervention at a pre-onset stage, in light of there being limited risk factors, unquantified sensitivity and specificity data for most putative onset illness risk factors, and thus there is the risk of overtreatment. Also, it is possible that many of the recommended interventions for those at risk of a bipolar disorder may have general non-specific benefits for those at risk. While retaining a pre-onset phase in the staging model, it would appear wiser for it to not be populated with recommended interventions until they have a firmer empirical base.

MEM-EX: An exemplar memory model of decisions from experience

Many real-world decisions must be made on basis of experienced outcomes. However, there is little consensus about the mechanisms by which people make these decisions from experience (DfE). Across five experiments, we identified several factors influencing DfE. We also introduce a novel computational modeling framework, the memory for exemplars model (MEM-EX), which posits that decision makers rely on memory for previously experienced outcomes to make choices. Using MEM-EX, we demonstrate how cognitive mechanisms provide intuitive and parsimonious explanations for the effects of value-ignorance, salience, outcome order, and sample size. We also conduct a cross-validation analysis of several models within the MEM-EX framework. We compare these to three alternative models; two baseline models built on the principle of expected value maximization, and another employing a suite of choice methods previously shown to perform well in prediction tournaments. We find that MEM-EX consistently outperforms these competitors, demonstrating its value as a tool for making quantitative predictions without overfitting. We discuss the implications of these findings for our understanding of the interplay between attention, memory, and experience-based choice.